In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Each tweet is annotated as no evidence of depression (e.g., “Citizens fear an economic depression") or evidence of depression (e.g., “depressed over disappointment"). If a tweet is annotated evidence of depression, then it is further annotated with one or more depressive symptoms, for example, depressed mood (e.g., “feeling down in the dumps"), disturbed sleep (e.g., “another restless night"), or fatigue or loss of energy (e.g., “the fatigue is unbearable") BIBREF10 . 
 Question: How is the dataset annotated?
no evidence of depression depressed mood disturbed sleep fatigue or loss of energy

To show the effectiveness of our approach, we show results on the SICK dataset BIBREF1, a common benchmark for logic-based NLI, and find MonaLog to be competitive with more complicated logic-based approaches (many of which require full semantic parsing and more complex logical machinery). 
 Question: Do they beat current state-of-the-art on SICK?
No

The SG model achieved a high average similarity score of 0.650 followed by CBoW with a 0.632 average similarity score. The GloVe also achieved a considerable average score of 0.591 respectively. However, the average similarity score of SdfastText is 0.388 and the word pair Microsoft-Bill Gates is not available in the vocabulary of SdfastText. Moreover, the average semantic relatedness similarity score between countries and their capitals is shown in Table TABREF78 with English translation, where SG also yields the best average score of 0.663 followed by CBoW with 0.611 similarity score. The GloVe also yields better semantic relatedness of 0.576 and the SdfastText yield an average score of 0.391. 
 Question: How does proposed word embeddings compare to Sindhi fastText word representations?

Proposed SG model vs SINDHI FASTTEXT:
Average cosine similarity score: 0.650 vs 0.388
Average semantic relatedness similarity score between countries and their capitals: 0.663 vs 0.391

